CN113110429A - Minimum lasting formation generation and control method of multi-robot system under visual field constraint - Google Patents

Minimum lasting formation generation and control method of multi-robot system under visual field constraint Download PDF

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CN113110429A
CN113110429A CN202110359512.7A CN202110359512A CN113110429A CN 113110429 A CN113110429 A CN 113110429A CN 202110359512 A CN202110359512 A CN 202110359512A CN 113110429 A CN113110429 A CN 113110429A
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CN113110429B (en
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杨庆凯
赵欣悦
李若成
刘奇
肖凡
方浩
辛斌
曾宪琳
陈杰
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Beijing Institute of Technology BIT
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Abstract

The invention discloses a minimum persistent formation generation and control method of a multi-robot system under visual field constraint, which relates to the technical field of multi-agent control. And then determining expected distances among the agents according to the expected formation information, and designing a gradient-based controller by the robot according to the expected distances and the neighbor states. On the basis of the controller, the visual field constraint is converted into the state constraint of the robot, and a controller with the constraint is designed by introducing a control barrier function, and the controller can enable the system to achieve the desired formation. The invention can generate the state observation topology with the simplest structure, and the robot can not lose neighbors due to the restriction of the perception range in the moving process, thereby ensuring the stability and the safety of formation.

Description

Minimum lasting formation generation and control method of multi-robot system under visual field constraint
Technical Field
The invention relates to the technical field of multi-agent control, in particular to a minimum lasting formation generation and control method of a multi-robot system under visual field constraint.
Background
In recent years, due to the wide application of multi-robot formation in scenes such as coordinated transportation, regional exploration in complex environments, industrial production and the like, research on multi-robot formation control has received great attention in academic and industrial fields. In the formation process, sensors are needed among robots to acquire information of neighbors, such as positions, speeds and the like. Nowadays, sensors mounted on robots tend to have a limited sensing range, for example: a limited field of view camera. In a traditional formation control method, visual field constraints of sensors are often ignored, and a sensing range is assumed to be free of dead angles. In the actual formation control, the method needs to assemble a plurality of sensors for the robots to ensure the sensing range, which not only increases the design cost of a single robot, but also greatly restricts the application range of multi-robot formation. Therefore, it is important to design new control methods to give the multi-robot system sensor the ability to form under the field of view constraint.
Disclosure of Invention
In view of the above, the invention provides a minimum persistent formation generation and control method for a multi-robot system under visual field constraint, which can generate a state observation topology with the simplest structure under the condition of limited sensor sensing range, and can realize formation of multiple robots without communication between the robots; and through the control method, the robot can not lose neighbors due to the restriction of the perception range in the moving process, so that the stability and the safety of formation are ensured.
In order to achieve the purpose, the technical scheme of the invention comprises the following steps:
aiming at a multi-robot system, generating a minimum persistent graph meeting the visual field constraint according to the initial position of the robot, taking the minimum persistent graph as an interactive topology, and distributing neighbors to each robot according to the interactive topology.
Determining an expected distance between robots according to expected formation information, designing a gradient-based controller by the robots according to the expected distance and a neighbor state, converting visual field constraints into state constraints of the robots on the basis of the gradient-based controller, introducing a control barrier function to constrain the gradient-based controller, and enabling a system to achieve the expected formation by using the finally obtained gradient-based controller with constraints.
Further, for a multi-robot system, according to the initial position of the robot, a minimum persistent graph satisfying the view constraint is generated, the minimum persistent graph is used as an interaction topology, and a neighbor is allocated to each robot according to the interaction topology, specifically:
first, an empty directed graph is initialized
Figure BDA0003004937220000021
Wherein
Figure BDA0003004937220000022
The point set is a point set, and the point set is a point set,
Figure BDA0003004937220000023
the epsilon is an edge set,
Figure BDA0003004937220000024
assuming that the limited visual field area is a sector area with a chord length d and a visual field angle alpha, the robots are numbered 0,1, …, n-1, and the total number of the robots is n.
Each robot corresponds to a vertex in the graph and has two degrees of freedom, and each degree of freedom represents the number of edges led out from the vertex.
And calculating the distance between every two robots according to the initial positions of the robots, wherein a candidate edge exists between the paired robots with the distance less than d, and recording the serial numbers of the paired robots with the distance less than d.
And sequentially executing the following edge adding operations on all candidate edges according to the sequence numbers of the vertexes of the candidate edges, wherein the specific flow is as follows:
for each candidate edge, if any of the two vertices of the current candidate edge is not in the directed graph
Figure BDA0003004937220000025
In (3), then the current candidate edge is directly added into the directed graph
Figure BDA0003004937220000026
In (3), the direction of the current candidate edge is set as not being in the directed graph
Figure BDA0003004937220000027
The vertex in (1) points to a directed graph
Figure BDA0003004937220000028
Vertex in (2), not in directed graph
Figure BDA0003004937220000029
Adding a directed graph to the vertices in (1)
Figure BDA00030049372200000210
If neither vertex of the current candidate edge is in the directed graph
Figure BDA00030049372200000211
In (3), adding both vertices into the directed graph
Figure BDA00030049372200000212
The direction is set arbitrarily.
If both vertices of the current candidate edge are in the graph
Figure BDA00030049372200000213
Then for each vertex, passThe method based on depth search carries out freedom degree search along a directed path taking a current vertex as a starting point, and judges whether a current candidate edge can be added into a directed graph or not after the freedom degree search
Figure BDA0003004937220000031
In (1).
After the edge adding operation is performed on all the candidate edges, if the directed graph
Figure BDA0003004937220000032
If 2n-3 edges exist in the graph, a minimum persistent graph meeting the visual field constraint is obtained, the minimum persistent graph is used as an interactive topology, and neighbors are distributed to each robot according to the interactive topology.
Further, if both vertices of the current candidate edge are in the graph
Figure BDA0003004937220000033
For each vertex, performing degree-of-freedom search along a directed path with the current vertex as a starting point by a depth search-based method, and judging whether the current candidate edge can be added into the directed graph or not after the degree-of-freedom search
Figure BDA0003004937220000034
The method comprises the following specific steps:
the deep search is divided into the following three cases;
case 1: when searching along a directed path with a vertex i as a starting point and a vertex k with a degree of freedom of 2 is encountered in the searching process, one degree of freedom is transferred from the vertex k to the vertex i by a method of reversing the edges (i, k).
Case 2: when searching along a directed path with a vertex i as a starting point, a vertex m with the degree of freedom of 1 is encountered in the searching process, which indicates that the vertex m already has an outwardly-led edge (m, n), and n is a neighbor of m; at this time, the angle between the side (m, i) and the side (m, n) is calculated, and if the angle is less than or equal to α, one degree of freedom can be transferred from the vertex m to the vertex i by reversing the side (i, m); if the angle is greater than alpha, then the process needs to continueContinuing to search for degrees of freedom in the direction of the edge (m, n), during the search, if one degree of freedom is transferred to the vertex m, further transferring one degree of freedom from the vertex m to the vertex i by reversing the direction of the edge (i, m), and if no degree of freedom is transferred to the vertex m, recording that the vertex m has a locked degree of freedom fixeddegreeThe lock degree of freedom represents the degree of freedom which can not be transferred through the path reversal, and the record candidate edge vertex i has a directed path fixed containing the lock degree of freedomedge。
Case 3: when searching along a directed path with a vertex i as a starting point, a vertex l with the degree of freedom of 0 is encountered in the searching process, and the vertex l is indicated to have two outwards-led edges (l, p) and (l, t), wherein p and t are neighbors of the vertex l; at the moment, respectively calculating an angle A between edges (l, i) and (l, p) and an angle B between the edges (l, i) and (l, t), if the angle A and the angle B are less than or equal to alpha, continuing searching along any one of the edges (l, p) and the edges (l, t), and if the degree of freedom can be found, transferring the degree of freedom to a peak i by an edge reverse method; if one of the angle A and the angle B is larger than alpha, namely the angle formed by the edges (l, i) and (l, p) is larger than alpha, continuing the search along the (l, p), and if one degree of freedom is transferred to the peak l in the search process, transferring the degree of freedom to the peak i by a method of reversing the edges (i, l); if (l, p) cannot find a degree of freedom and then search along the edge (l, t), if a degree of freedom can be transferred to l, record l with a locked degree of freedom fixeddegreeVertex i has a directed path fixed containing lock degrees of freedomedge(ii) a If both the < A and the < B are more than alpha, searching the freedom degree along the directions of the two edges, and if two freedom degrees exist and can be transferred to l, transferring any one freedom degree to i by reversing the edges (i, l).
In the above depth search process, all vertices can only be searched once, and after four depth searches are performed on one candidate edge, if: fixeddegree+numberverIs greater than or equal to 4, and fixededge+numberverWhen 4, the candidate edge is added to the graph
Figure BDA0003004937220000041
Wherein numberverRepresenting the number of degrees of freedom of the vertices of the candidate edge.
Further, the vertex-associated robot with the degree of freedom 2 is used as a navigator, and the other robots are used as followers.
Further, according to the expected formation information, the expected distance between the robots is determined, and the robots design gradient-based controllers according to the expected distance and the neighbor state, specifically:
Figure BDA0003004937220000042
ωi＝-k(θi-θ*),
wherein a isijRepresenting the connection relation between the robot i and the neighbor robot j, wherein j belongs to 1, …, N and N represents the number of neighbors of the robot i.
Further, on the basis of a gradient-based controller, visual field constraint is converted into state constraint of the robot, a control barrier function is introduced to constrain the gradient-based controller, and the finally obtained gradient-based controller with constraint is used for enabling the system to achieve a desired formation, specifically to achieve the desired formation
On the basis of a gradient-based controller, converting the visual field constraint into a state constraint of the robot,
C＝{q∈R2:h(q)≥0},
Figure BDA0003004937220000051
Int(C)＝{q∈R2:h(q)>0}.
h1(q)＝(-1)0×(R2-‖q-q1‖2)>0,
h2(q)＝(-1)0×(R2-‖q-q2‖2)>0,
h3(q)＝(-1)0×(r2-‖q-qo‖2)>0,
wherein C is a state safety set constructed according to the sensing radius d and the view angle alpha, namely the robot can simultaneously see 2 neighbors in the area range of the set C;
Figure BDA0003004937220000052
represents the boundary of the set C, int (C) represents the interior of the set C; q is the robot position; h (q) is a scalar function and is divisible into three secure sets h1(q)、h2(q)、h3(q)，h(q)＝h1(q)h2(q)h3(q)；qoThe circle center of the blind area is calculated by the view angle alpha and the positions of two neighbors; q. q.s1And q is2The positions of two neighbors are respectively;
controller with constraints designed by introducing control barrier function
Figure BDA0003004937220000053
Figure BDA0003004937220000054
Figure BDA0003004937220000055
Figure BDA0003004937220000056
Figure BDA0003004937220000057
Wherein u isiInputting the speed for the control of the robot i;
Figure BDA0003004937220000058
a nominal formation controller; alpha (h)1(q))＝γ×(h1(q))3(ii) a Gamma is a constant number, gamma>0；Lfh1(q),Lgh1(q) are all h1The lie derivative of (q).
Under the control of a gradient-based controller with constraints, the multi-robot system forms a desired formation in the presence of field-of-view constraints.
Has the advantages that:
firstly, according to the initial position of the robot, an interactive topology meeting the visual field constraint is generated by adopting a depth search-based method, and neighbors are distributed to each robot according to the interactive topology, so that each robot can meet the visual field constraint. And then determining expected distances among the agents according to the expected formation information, and designing a gradient-based controller by the robot according to the expected distances and the neighbor states. On the basis of the controller, the visual field constraint is converted into the state constraint of the robot, and a controller with the constraint is designed by introducing a control barrier function, and the controller can enable the system to achieve the desired formation. Compared with some traditional control methods which need to design a perception topology for the robot in advance and determine the neighbor relation, the method disclosed by the invention can automatically generate the perception topology meeting the visual field constraint according to the initial position of the robot, so that the artificial design is reduced, and a multi-robot system has stronger adaptability under different environments.
In practical application, the method does not need to assemble a large number of sensors for the robot to acquire information and communicate, only one camera is needed to complete formation control, hardware cost is reduced, and the applicable range of multi-robot formation is expanded.
The control method designed by the invention can ensure that the robot keeps the communication of the perception topology in the movement process, namely, the robot can always perceive the information of the neighbors, thereby improving the stability and the safety of formation.
Drawings
FIG. 1 is a diagram of formation under view constraints.
FIG. 2 is a schematic diagram of regions that satisfy a field of view constraint.
Fig. 3 is a perception topology generated by the algorithm of the robot at an initial position.
FIG. 4 is a diagram of the effect of a conventional queuing algorithm in the presence of a view constraint.
FIG. 5 is a diagram of the effect of the algorithm of the present invention in the presence of a visual field constraint.
FIG. 6 is a diagram of the effect of formation of a multi-robot system under the control algorithm of the present invention.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
The invention provides a minimum persistent formation generation and control method of a multi-robot system under visual field constraint, which comprises the following detailed implementation modes:
step one, generating a topological relation meeting the visual field constraint according to the initial position of the robot.
The specific process is as follows:
the invention contemplates a multi-robot system consisting of n robots moving on a two-dimensional plane. The kinetic model of which is considered to be a first order model, i.e.
Figure BDA0003004937220000071
Figure BDA0003004937220000072
Wherein q isiRepresenting the coordinates of the robot i on a two-dimensional plane
Figure BDA0003004937220000073
Speed; u. ofiIs the input speed; omegaiIs the input angular velocity; thetaiThe angle of the visual direction of the robot i relative to the x axis of a world coordinate system is defined as the world coordinate system constructed in a two-dimensional plane; i is the serial number of the robot and takes the value of 1 to n, namely n robots
Using directed graphs
Figure BDA0003004937220000074
To represent an interaction network between robots, wherein a set of points
Figure BDA0003004937220000075
Edge set
Figure BDA0003004937220000076
The edge sets are all directed edges; adjacency matrix
Figure BDA0003004937220000077
Figure BDA0003004937220000078
Representing a matrix of real numbers of n x n.
If it is not
Figure BDA0003004937220000079
Then aij0, otherwise aij1. (i, j) e epsilon means that robot i can perceive robot j, measuring the relative position with robot j. Set of all neighbors of robot i
Figure BDA00030049372200000710
Figure BDA00030049372200000711
And (4) showing. FIG. 1 is a diagram of formation of a multi-robot system in the presence of field-of-view constraints. The existence of the visual field constraint requires that each robot can form a desired formation by sensing the state of the neighbor under the condition of satisfying the constraint condition, namely, the length of each edge in the graph is smaller than the sensing distance, and each angle is smaller than the visual field constraint of the robot. The field of view constraint includes a perceived distance and a perceived angle range.
Step one of the present invention, for a stacked robot system, when the initial positions of all robots are known, a directed graph satisfying the view constraints can be generated, and the directed graph is a minimum persistent graph, that is, when each robot keeps a desired distance from its neighbors, the overall formation of the system remains unchanged and the number of required edges is minimum.
A detailed description of the generation topology algorithm is given below:
first, an empty directed graph is initialized
Figure BDA0003004937220000081
Wherein
Figure BDA0003004937220000082
Assuming that the limited field of view is a sector area with a chord length d and an angle α, the robots are numbered 0,1, …, n-1, each robot corresponds to a vertex in the figure and has two degrees of freedom, each degree of freedom represents the number of edges that can be drawn outward from the vertex, and for example, two degrees of freedom can be considered to be translation and rotation. And calculating the distance between every two robots according to the initial positions of the robots, recording the serial numbers of the paired robots with the distance less than d, and representing that a candidate edge exists. For each candidate edge, if any of the two vertices is not in the graph
Figure BDA0003004937220000083
Then the edge can be added directly (it is required that the number of edges of any sub-graph cannot exceed 2n-3), the direction is that the vertex not in the graph points to the vertex in the graph, the vertex not in the graph can be added, if the directions of both are not arbitrarily set in the directed graph. If two vertices i, j of the candidate edge are in the graph
Figure BDA0003004937220000084
For each vertex, a degree-of-freedom search needs to be performed along a directional path with the vertex as a starting point by a depth search-based method, and the search can be divided into the following three cases.
Case 1: when searching along a directed path with a vertex i as a starting point, a vertex k with a degree of freedom of 2 is encountered in the searching process, and one degree of freedom can be transferred from the vertex k to the vertex i by a method of reversing the edge (i, k), so that the edge (i, k) reversal set is to convert the direction of the edge into a k direction i.
Case 2: when following the line starting from the vertex iWhen searching for a path, a vertex m with the degree of freedom of 1 is encountered in the searching process, which indicates that the vertex has an outwardly-led edge (m, n), and n is a neighbor of m. At this time, the angle between the side (m, i) and the side (m, n) is calculated, and if the angle is less than or equal to α, one degree of freedom can be transferred from the vertex m to the vertex i by reversing the side (i, m); if the angle is larger than alpha, the freedom degree needs to be continuously searched along the direction of the edge (m, n), if one freedom degree can be transferred to the vertex m, one freedom degree can be transferred from the vertex m to the vertex i by a method of reversing the edge (i, m), if no freedom degree can be transferred to the vertex m, the vertex m is recorded to have a locking freedom degree fixeddegree(the lock degree of freedom represents the degree of freedom that cannot be transferred through the path reversal), and the record candidate edge vertex i has a directed path fixed containing the lock degree of freedomedge。
Case 3: when searching along the directional path with the vertex i as the starting point, a vertex l with the degree of freedom of 0 is encountered in the searching process, which indicates that two outwards-drawn edges (l, p) and (l, t) exist in the vertex, and p, t are neighbors of the vertex l. At this time, an angle & lt A between edges (l, i) and (l, p) and an angle & lt B between edges (l, i) and (l, t) are respectively calculated, if the angle & lt A and the angle & lt B are both less than or equal to alpha, then the search can be continued along any edge between (l, p) and (l, t), and if the degree of freedom can be found, the degree of freedom is transferred to a peak i by an edge reversal method. If one of the two angles of & B is greater than a, for example: if one degree of freedom can be transferred to the vertex i, the degree of freedom can be transferred to the vertex i by reversing the direction of the edge (i, l); if (l, p) cannot find a degree of freedom and then search along the edge (l, t), if a degree of freedom can be transferred to l, record l has a fixeddegreeVertex i has a fixededge. If both the angle A and the angle B are larger than alpha, searching for the degree of freedom along the directions of two edges is carried out, and if the two degrees of freedom can be transferred to l, any one degree of freedom can be transferred by reversing the edges (i, l)To i.
In the recursive search procedure above, all vertices can only be searched once. The invention provides a proof of generating a topological algorithm: fixed if and only if after using four search algorithms for a candidate edgedegree+numberverIs greater than or equal to 4, and fixededge+numberverWhen it is 4 ═ numberverNumber of end-point degrees of freedom representing a candidate edge) that candidate edge may be added to the graph
Figure BDA0003004937220000091
Performing the following steps; after the edge adding operation is performed on all the candidate edges, if the graph is
Figure BDA0003004937220000092
With 2n-3 edges in it, the topology is successfully generated. The topology can satisfy the visual field constraint of the robot and provide a neighbor relation for subsequent formation control. If the 2n-3 is not reached, the topology cannot be generated.
And step two, determining the expected distance between the intelligent agents according to the information of the expected formation, and designing a controller to form the expected formation.
After generating the topological relation meeting the constraint condition, the robot determines the neighbors according to the topology, and defines the expected distance between the robot and the neighbors according to the expected formation as follows
dij＝||qi-qj||.
Defining a relative position gamma between the robot and its neighboursij
γij＝qi-qj,
Control input for each navigator (vertex correspondence robot with degree of freedom 2)
Figure BDA0003004937220000101
ω i0, i is navigator
For each follower (robot with no or one degree of freedom), the following gradient-based nominal formation controller is designed
Figure BDA0003004937220000102
Wherein a isijAnd j ∈ 1, …, and N represents the number of neighbors of the follower robot i.
In addition, an angular control rate of the follower is defined
ωi＝-k(θi-θ*),
Wherein k is>0 is the control gain, θiIs the current angle, theta, of the robot*The desired angle is defined as the direction of the bisector of the angle that the robot makes with its two neighbors.
On the basis of the control rate, the connectivity maintenance problem of the graph in the formation process is considered, and the visual field constraint is converted into the state constraint of the robot by introducing a control barrier function (zeroing control barrier function), and the specific steps are as follows:
given the size of the perceived radius d and the viewing angle a, a state "safe" set C can be constructed, as shown in region iv of fig. 2 (the robot cannot guarantee two neighbors are seen at the same time at each moment in the remaining three regions).
The set C is defined as
C＝{q∈R2:h(q)≥0},
Figure BDA0003004937220000103
Int(C)＝{q∈R2:h(q)>0}.
Wherein
Figure BDA0003004937220000111
Represents the boundaries of the collection, int (c) represents the interior of the collection, h (q) is a scalar function and is derivable; q is the robot position.
By "safe" is meant here that the robot can see both of its neighbors and acquire information in the area at the same time. The area IV is the intersection part of circles which take two neighbors as circle centers and have the radius of dA region formed by a circle with a hatched portion removed, h (q) h1(q)h2(q)h3(q), three safety sets the mathematical expression of which is as follows
h1(q)＝(-1)0×(R2-‖q-q1(t)‖2)>0,
h2(q)＝(-1)0×(R2-‖q-q2(t)‖2)>0,
h3(q)＝(-1)1×(r2-‖q-qo(t)‖2)>0,
Wherein q isoIs the center of the blind spot, which can be calculated from the angle alpha and the positions of the two neighbors. q is an independent variable, q1(t) the position of the first neighbor, R being the sensing radius
Wherein the radius is the radius of the blind zone
Figure BDA0003004937220000112
The circle center is as follows: dead zone centre of a circle
Figure BDA0003004937220000113
Where the intermediate variable h is r × cos α, xi,yiRepresents the horizontal and vertical coordinates (note: q)1(t)＝[x1,y1]T,q2(t)＝[x2,y2]TRepresenting the positions of two neighbors).
According to the above definition, the control law of each robot is as follows in consideration of the constraints imposed by the field of view
Figure BDA0003004937220000114
Figure BDA0003004937220000115
Figure BDA0003004937220000116
Figure BDA0003004937220000117
Wherein u isiInputting the speed for the control of the robot i;
Figure BDA0003004937220000121
a nominal formation controller; alpha (h)1(q))＝γ×(h1(q))3(ii) a Gamma is a constant number, gamma>0；Lfh1(q),Lgh1(q) are all h1The lie derivative of (q).
According to the above control law
Figure BDA0003004937220000122
When the desired formation does not conflict with the field of view constraints, the multi-robot system can form the desired formation in the presence of the field of view constraints.
Next, the present invention performed simulation experiments on the proposed formation generation and control method. Three experiments were performed in the present invention: the first group of experiments are used for verifying a persistent graph generation algorithm, and given the initial position of the robot, a minimum persistent graph meeting constraint conditions can be generated; the second group of experiments are used for verifying connectivity maintenance of the control rate, the invention considers the conditions of three agents, and verifies that the algorithm can ensure that the neighbor is always in the visual field range of the robot; the third set of experiments used the topology generated by the first set of experiments to verify that the multi-robot system was able to form the desired formation.
Fig. 3 shows that the initial positions of the agents are: q. q.s0＝[0,0]T,q1＝[1,-1]T,q2＝[1,1]T,q3＝[0,2]T,q4＝[-1,0.5]T,q5＝[-1,-0.5]T,q6＝[1.5,0]TThe perception distance d is 2, seeThe field angle α is the minimum persistence map generated by the algorithm when the field angle α is 90 °, and it can be seen that the angle between two neighbors of each robot satisfies the angle constraint of the robot itself.
Fig. 4 and 5 show the results of the second set of experiments. When the static initial positions of two neighbors are respectively [1,1 ]]TAnd [3,1]TWhen the initial position of the robot is [3.5,0.6 ]]TLet γ equal to 0.8, the desired distances are 1.5 and 2.8, respectively. As can be seen from FIG. 4, use
Figure BDA0003004937220000123
As control input, the robot walks into the shadow area at 0.4s, and at this time, two neighbors cannot be seen at the same time; and figure 5 uses
Figure BDA0003004937220000124
The state of the robot can be always in a 'safe' set, namely two neighbors can be seen all the time, and the expected formation is realized.
Fig. 6 shows a process of forming a desired formation by a multi-robot system composed of 9 robots. The topology on the right is the minimum persistent graph formed at the initial position that satisfies the constraints, the topology on the left shows that the robot formation has formed the desired formation, and the dotted lines represent the trajectory of each robot. Where the desired distance between agents is 3. To facilitate the display of the formation process, each robot is given an additional speed of [ -1,0.3sin (0.8t)]TIt can be seen that the multi-agent system is generating topology and control rate from initial position
Figure BDA0003004937220000131
To the desired formation and the field constraints can be satisfied at all times during the process.
Through simulation verification, the minimum persistent formation generation and control method of the multi-robot system under the visual field constraint can be used for completing formation generation and control of multiple robots under the condition that the robots have a limited sensing area. In addition, the algorithm can ensure that the perception topology of the multi-robot system is always connected in the formation generation and control process, and the formation safety is enhanced.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (6)

1. The minimum persistent formation generation and control method of the multi-robot system under the visual field constraint is characterized by comprising the following steps:
aiming at a multi-robot system, generating a minimum persistent graph meeting the visual field constraint according to the initial position of the robot, taking the minimum persistent graph as an interactive topology, and distributing neighbors to each robot according to the interactive topology;
determining an expected distance between robots according to expected formation information, designing a gradient-based controller by the robots according to the expected distance and a neighbor state, converting visual field constraints into state constraints of the robots on the basis of the gradient-based controller, introducing a control barrier function to constrain the gradient-based controller, and enabling a system to achieve the expected formation by using the finally obtained gradient-based controller with constraints.
2. The method according to claim 1, wherein for a multi-robot system, a minimum persistent graph satisfying a view constraint is generated from an initial position of a robot, and the minimum persistent graph is taken as an interaction topology according to which neighbors are assigned to each robot, specifically:
first, an empty directed graph is initialized
Figure FDA0003004937210000011
Wherein
Figure FDA0003004937210000012
The point set is a point set, and the point set is a point set,
Figure FDA0003004937210000013
the epsilon is an edge set,
Figure FDA0003004937210000014
assuming that the limited visual field area is a sector area with chord length d and visual field angle alpha, the robot number is 0,1, …, n-1, and the total number of the robots is n;
each robot corresponds to a vertex in the graph and has two degrees of freedom, and each degree of freedom represents the number of edges led out from the vertex;
calculating the distance between every two robots according to the initial positions of the robots, wherein a candidate edge exists between the paired robots with the distance less than d, and recording the serial numbers of the paired robots with the distance less than d;
and sequentially executing the following edge adding operations on all candidate edges according to the sequence numbers of the vertexes of the candidate edges, wherein the specific flow is as follows:
for each candidate edge, if any of the two vertices of the current candidate edge is not in the directed graph
Figure FDA0003004937210000015
Then the current candidate edge directly joins the directed graph
Figure FDA0003004937210000016
In (3), the direction of the current candidate edge is set as not being in the directed graph
Figure FDA0003004937210000021
The vertex in (1) points to a directed graph
Figure FDA0003004937210000022
Vertex in (2), not in directed graph
Figure FDA0003004937210000023
Adding a directed graph to the vertices in (1)
Figure FDA0003004937210000024
If neither vertex of the current candidate edge is in the directed graph
Figure FDA0003004937210000025
In (3), adding both vertices into the directed graph
Figure FDA0003004937210000026
The direction is set arbitrarily;
if both vertices of the current candidate edge are in the graph
Figure FDA0003004937210000027
Then, for each vertex, performing a degree-of-freedom search along a directed path with the current vertex as a starting point by a depth search-based method, and after the degree-of-freedom search, determining whether a current candidate edge can be added to the directed graph
Figure FDA0003004937210000028
Performing the following steps;
after performing the edge-adding operation on all the candidate edges, if the directed graph
Figure FDA0003004937210000029
If 2n-3 edges exist in the graph, a minimum persistent graph meeting the visual field constraint is obtained, the minimum persistent graph is used as an interactive topology, and neighbors are distributed to each robot according to the interactive topology.
3. The method of claim 2, wherein if both vertices of the current candidate edge are on the graph
Figure FDA00030049372100000210
Then, for each vertex, performing a degree-of-freedom search along a directed path with the current vertex as a starting point by a depth search-based method, and after the degree-of-freedom search, determining whether a current candidate edge can be added to the directed graph
Figure FDA00030049372100000211
The method comprises the following specific steps:
the depth search is divided into the following three cases;
case 1: when searching along a directed path with a vertex i as a starting point and encountering a vertex k with a degree of freedom of 2 in the searching process, transferring one degree of freedom from the vertex k to the vertex i by a method of reversing the edges (i, k);
case 2: when searching along a directed path with a vertex i as a starting point, a vertex m with the degree of freedom of 1 is encountered in the searching process, which indicates that the vertex m already has an outwardly-led edge (m, n), and n is a neighbor of m; at this time, the angle between the side (m, i) and the side (m, n) is calculated, and if the angle is less than or equal to α, one degree of freedom can be transferred from the vertex m to the vertex i by reversing the side (i, m); if the angle is larger than alpha, the freedom degree needs to be continuously searched along the direction of the edge (m, n), in the searching process, if one freedom degree is transferred to the vertex m, one freedom degree is further transferred to the vertex i from the vertex m by a method of reversing the edge (i, m), if no freedom degree is transferred to the vertex m, the vertex m is recorded to have a locking freedom degree fixeddegreeThe lock degree of freedom represents the degree of freedom which can not be transferred through the path reversal, and the record candidate edge vertex i has a directed path fixed containing the lock degree of freedomedge；
Case 3: when searching along a directed path with a vertex i as a starting point, a vertex l with the degree of freedom of 0 is encountered in the searching process, and the vertex l is indicated to have two outwards-led edges (l, p) and (l, t), wherein p and t are neighbors of the vertex l; at the moment, respectively calculating an angle A between edges (l, i) and (l, p) and an angle B between the edges (l, i) and (l, t), if the angle A and the angle B are less than or equal to alpha, continuing searching along any one of the edges (l, p) and the edges (l, t), and if the degree of freedom can be found, transferring the degree of freedom to a peak i by an edge reverse method; if one of the < A and the < B is larger than alpha, namely the angle formed by the edges (l, i) and (l, p) is larger than alpha, then the search is continued along (l, p), and if one degree of freedom is transferred to the search processOn the vertex i, the degree of freedom is transferred to the vertex i by a method of reversing the edges (i, l); if (l, p) cannot find a degree of freedom and then search along the edge (l, t), if a degree of freedom can be transferred to l, record l with a locked degree of freedom fixeddegreeVertex i has a directed path fixed containing lock degrees of freedomedge(ii) a If both the < A and the < B are greater than alpha, searching the freedom degree along the directions of the two edges, and if two freedom degrees exist and can be transferred to l, transferring any one freedom degree to i by reversing the edges (i, l);
in the above depth search process, all vertices can only be searched once, and after four depth searches are performed on one candidate edge, if: fixeddegree+numberverIs greater than or equal to 4, and fixededge+numberverWhen 4, the candidate edge is added to the graph
Figure FDA0003004937210000031
Wherein numberverRepresenting the number of degrees of freedom of the vertices of the candidate edge.
4. The method of claim 3, wherein the vertex with degree of freedom 2 corresponds to a robot as a pilot and other robots as followers.
5. The method according to any one of claims 1 to 4, wherein the expected distance between robots is determined according to the expected formation information, and the robots design a gradient-based controller according to the expected distance and the neighbor state, specifically:
Figure FDA0003004937210000041
ωi＝-k(θi-θ*),
wherein a isijRepresenting the connection relation between the robot i and the neighbor robot j, wherein j belongs to 1, …, N and N represents the number of neighbors of the robot i.
6. The method according to claim 5, wherein the field of view constraint is converted into a state constraint of the robot based on the gradient-based controller, a control barrier function is introduced to constrain the gradient-based controller, and the resulting constrained gradient-based controller is used to achieve a desired formation for the system, specifically:
converting, on the basis of the gradient-based controller, a field-of-view constraint into a state constraint of the robot, C ═ q ∈ R2:h(q)≥0},
Figure FDA0003004937210000042
Int(C)＝{q∈R2:h(q)>0}.
h1(q)＝(-1)0×(R2-‖q-q1‖2)>0,
h2(q)＝(-1)0×(R2-‖q-q2‖2)>0,
h3(q)＝(-1)0×(r2-‖q-qo‖2)>0,
Wherein C is a state safety set constructed according to the sensing radius d and the view angle alpha, namely the robot can simultaneously see 2 neighbors in the area range of the set C;
Figure FDA0003004937210000044
represents the boundary of the set C, int (C) represents the interior of the set C; q is the robot position; h (q) is a scalar function and is divisible into three secure sets h1(q)、h2(q)、h3(q)，h(q)＝h1(q)h2(q)h3(q)；qoThe circle center of the blind area is calculated by the view angle alpha and the positions of two neighbors; q. q.s1And q is2The positions of two neighbors are respectively;
design with constraints introducing control barrier functionController
Figure FDA0003004937210000043
Figure FDA0003004937210000051
Figure FDA0003004937210000052
Figure FDA0003004937210000053
Figure FDA0003004937210000054
Wherein u isiInputting the speed for the control of the robot i;
Figure FDA0003004937210000055
a nominal formation controller; alpha (h)1(q))＝γ×(h1(q))3(ii) a Gamma is a constant number, gamma>0；Lfh1(q),Lgh1(q) are all h1A lie derivative of (q);
under the control of a gradient-based controller with constraints, the multi-robot system forms a desired formation in the presence of field-of-view constraints.
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